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Marinus, J.

Publications and source records attributed to Marinus, J..

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Parkinson disease age of onset GWAS: defining heritability, genetic loci and a-synuclein mechanisms

Increasing evidence supports an extensive and complex genetic contribution to Parkinsons disease (PD). Previous genome-wide association studies (GWAS) have shed light on the genetic basis of risk for this disease. However, the genetic determinants of PD age of onset are largely unknown. Here we performed an age of onset GWAS based on 28,568 PD cases. We estimated that the heritability of PD age of onset due to common genetic variation was ~0.11, lower than the overall heritability of risk for PD (~0.27) likely in part because of the subjective nature of this measure. We found two genome-wide significant association signals, one at SNCA and the other a protein-coding variant in TMEM175, both of which are known PD risk loci and a Bonferroni corrected significant effect at other known PD risk loci, INPP5F/BAG3, FAM47E/SCARB2, and MCCC1. In addition, we identified that GBA coding variant carriers had an earlier age of onset compared to non-carriers. Notably, SNCA, TMEM175, SCARB2, BAG3 and GBA have all been shown to either directly influence alpha-synuclein aggregation or are implicated in alpha-synuclein aggregation pathways. Remarkably, other well-established PD risk loci such as GCH1, MAPT and RAB7L1/NUCKS1 (PARK16) did not show a significant effect on age of onset of PD. While for some loci, this may be a measure of power, this is clearly not the case for the MAPT locus; thus genetic variability at this locus influences whether but not when an individual develops disease. We believe this is an important mechanistic and therapeutic distinction. Furthermore, these data support a model in which alpha-synuclein and lysosomal mechanisms impact not only PD risk but also age of disease onset and highlights that therapies that target alpha-synuclein aggregation are more likely to be disease-modifying than therapies targeting other pathways.

genetics

Peripheral mitochondrial function correlates with clinical severity in idiopathic Parkinson’s disease

BackgroundParkinson disease is an intractable disorder with heterogeneous clinical presentation that may reflect different underlying pathogenic mechanisms. Surrogate indicators of pathogenic processes correlating with clinical measures may assist in better patients stratification. Mitochondrial function - which is impaired in and central to PD pathogenesis - may represent one of such surrogate indicators.\n\nMethodsMitochondrial function was assessed by respirometry experiment in fibroblasts derived from idiopathic patients (n=47) in normal conditions and in experimental settings that do not permit glycolysis and therefore force energy production through mitochondrial function. Respiratory parameters and clinical measures were correlated with bivariate analysis. Machine learning based classification and regression trees were used to classify patients on the basis of biochemical and clinical measures. Effects of mitochondrial respiration on alpha-synuclein stress was assessed monitoring the protein phosphorylation in permitting versus restrictive glycolysis conditions.\n\nResultsBioenergetics properties in peripheral fibroblasts correlate with clinical measures in idiopathic patients and correlation is stronger with predominantly non-dopaminergic signs. Bioenergetics analysis under metabolic stress, in which energy is produced solely by mitochondria, shows that patients fibroblasts can augment respiration, therefore indicating that mitochondrial defects are reversible. Forcing energy production through mitochondria, however, favors alpha-synuclein stress in different cellular experimental systems. Machine learning-based classification identified different groups of patients in which increasing disease severity parallels higher mitochondrial respiration.\n\nConclusionSuppression of mitochondrial activity in Parkinson disease may be an adaptive strategy to cope with concomitant pathogenic factors. Moreover, mitochondrial measures are potential biomarkers to follow disease progression.

neuroscience